Machine learning techniques for short-term load forecasting
| Autor: | Marijana Cosovic, Elvisa Becirovic |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Computer science business.industry 020209 energy Load forecasting 020208 electrical & electronic engineering 02 engineering and technology Machine learning computer.software_genre Term (time) Task (project management) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electricity Regression algorithm business computer Physics::Atmospheric and Oceanic Physics Selection (genetic algorithm) |
| Zdroj: | 2016 4th International Symposium on Environmental Friendly Energies and Applications (EFEA). |
| DOI: | 10.1109/efea.2016.7748789 |
| Popis: | Selection of an adequate tool for accurate short-term load forecasting task is becoming more important for electric utilities. Machine learning techniques are proving useful for short-term electricity load forecasting. In this paper we evaluate performance of several machine learning algorithms applied to electricity load datasets. We evaluated performance of SMOreg, and Additive regression algorithms for load forecasting using electricity consumption datasets. We also performed an Artificial Neural Networks (ANN) analysis on short-term load forecasting. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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